# Incorporating Broad Phonetic Information for Speech Enhancement

arXiv: https://arxiv.org/abs/2008.07618 · HTML: https://mlsanity.com/paper/2008.07618/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [The Role of Phonetic Units in Speech Emotion Recognition](https://mlsanity.com/paper/2108.01132/) (2021)
- [A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement](https://mlsanity.com/paper/2403.01369/) (2024)
- [Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula](https://mlsanity.com/paper/2306.06865/) (2023)
- [Phonetic Error Analysis of Raw Waveform Acoustic Models with Parametric and Non-Parametric CNNs](https://mlsanity.com/paper/2406.00898/) (2024)
- [Phonetic Error Analysis of Raw Waveform Acoustic Models](https://mlsanity.com/paper/2606.07030/) (2026)

## Abstract (arXiv)

In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals. Previous studies have also confirmed the benefits of incorporating phonetic information in a speech enhancement (SE) system to achieve better denoising performance. To obtain the phonetic information, we usually prepare a phoneme-based acoustic model, which is trained using speech waveforms and phoneme labels. Despite performing well in normal noisy conditions, when operating in very noisy conditions, however, the recognized phonemes may be erroneous and thus misguide the SE process. To overcome the limitation, this study proposes to incorporate the broad phonetic class (BPC) information into the SE process. We have investigated three criteria to build the BPC, including two knowledge-based criteria: place and manner of articulatory and one data-driven criterion. Moreover, the recognition accuracies of BPCs are much higher than that of phonemes, thus providing more accurate phonetic information to guide the SE process under very noisy conditions. Experimental results demonstrate that the proposed SE with the BPC information framework can achieve notable performance improvements over the baseline system and an SE system using monophonic information in terms of both speech quality intelligibility on the TIMIT dataset.